Enrolment and Baseline Summaries

Exploratory summaries of enrolments and baseline data.

Author

James Totterdell

Published

July 14, 2022

Load packages

library(tidyverse)
library(patchwork)
library(DT)
library(plotly)
library(knitr)
library(lubridate)
library(kableExtra)

theme_set(theme_classic(base_size = 10, base_family = "Palatino") +
  theme(panel.grid = element_blank(),
        strip.background = element_blank()))

Load data

devtools::load_all()
all_data <- read_all_no_daily()
all_daily_data <- read_all_daily()

Enrolment

Study enrolment

md <- get_interim_dates()
id <- get_intervention_dates()
p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(RandDate) %>%
  complete(RandDate = 
             seq.Date(min(RandDate, na.rm = TRUE), 
                      as.Date("2022-04-09"), 
                      by = "1 day"),
           fill = list(n = 0)) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`)) +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  labs(x = "", subtitle = "Cumulative enrolments", y = "")

p2 <- id %>%
  mutate(
    Intervention = fct_inorder(val_labels(Intervention))
  ) %>%
  ggplot(.) +
  facet_grid(Domain ~ ., drop = TRUE, scales = "free_y", space = "free_y") +
  geom_point(data = . %>% filter(Domain != "Antiviral"),
             aes(x = Intervention, y = endate), shape = 4) +
  geom_segment(
    aes(x = Intervention, xend = Intervention, 
        y = stdate, yend = endate))  +
  geom_hline(data = md,
             aes(yintercept = as.numeric(meet_date)),
             linetype = 2) +
  coord_flip() +
  labs(x = "", y = "Calendar date", subtitle = "Available interventions")

p <- p1 / p2
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-overall.pdf"), p, height = 4.5, width = 6)
p

Figure 1: Cumulative enrolments to the platform, vertical lines indicate timing of interim analyses

Study enrolment

p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, RandDate) %>%
  complete(
    Country, 
    RandDate = seq.Date(min(RandDate, na.rm = T), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  ungroup() %>%
  mutate(Country = factor(Country, levels = c("India", "Australia", "Nepal", "New Zealand"))) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`, fill = Country)) +
  # facet_wrap( ~ Country, ncol = 1, scales = "free_y") +
  geom_area(position = "stack") +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_fill_viridis_d("", begin = 0.2, end = 0.8, option = "B") +
  labs(x = "", subtitle = "Cumulative enrolments", y = "") +
  theme(legend.position = "top",
        legend.key.size = unit(0.7, "lines"),
        legend.text = element_text(size = rel(0.8)))

p3 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, RandDate) %>%
  complete(
    Country, 
    RandDate = seq.Date(min(RandDate, na.rm = T), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  ungroup() %>%
  mutate(Country = factor(Country, levels = c("India", "Australia", "Nepal", "New Zealand"))) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`, group = Country)) +
  # facet_wrap( ~ Country, ncol = 1, scales = "free_y") +
  geom_step(position = "stack") +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_fill_viridis_d("", begin = 0.2, end = 0.8, option = "B") +
  labs(x = "", subtitle = "Cumulative enrolments", y = "") +
  theme(legend.position = "top",
        legend.key.size = unit(0.7, "lines"),
        legend.text = element_text(size = rel(0.8)))

p <- p1 / p2
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-overall-country.pdf"), p, height = 5, width = 6)
p

Figure 2: Cumulative enrolments to the platform, vertical lines indicate timing of interim analyses

Study enrolment by country

p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, RandDate) %>%
  complete(
    Country, 
    RandDate = seq.Date(min(RandDate, na.rm = T), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Randomisation date` = RandDate) %>%
  ggplot(., aes(`Randomisation date`, `Cumulative enrolments`)) +
  facet_wrap( ~ Country, ncol = 1, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  labs(x = "", subtitle = "Cumulative enrolments", y = "")
p <- (p1 / p2) + plot_layout(heights = c(5, 2))
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-country.pdf"), p, height = 6, width = 6)
p

Figure 3: Cumulative enrolments to the platform by country, vertical lines indicate timing of interim analyses

Study enrolment by site

p1 <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0") %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(min(RandDate), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0)) %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ paste(Country, Site, sep = ": "), 
              ncol = 5, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1)))))
ggplotly(p1, height = 1000)

Code

p_india <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0", PT_CountryName == "India") %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(min(RandDate), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0))  %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ Site, 
              ncol = 3, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  scale_x_date(date_breaks  = "4 months", date_labels = "%b %Y") +
  theme(strip.text = element_text(size = rel(0.7)))

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-india-sites.pdf"), p_india, height = 5, width = 7)

Code

p_aus <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0", PT_CountryName == "Australia") %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(min(RandDate), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0))  %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ Site, 
              ncol = 3, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  scale_x_date(date_breaks  = "4 months", date_labels = "%b %Y") +
  theme(strip.text = element_text(size = rel(0.7)))

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-australia-sites.pdf"), p_aus, height = 5, width = 7)

Code

p_npnz <- all_data %>%
  filter(ENR_rec == 1, CAssignment != "C0", PT_CountryName %in% c("Nepal", "New Zealand")) %>%
  dplyr::count(Country = PT_CountryName, Site = PT_LocationName, RandDate) %>%
  complete(
    nesting(Country, Site),
    RandDate = seq.Date(as.Date("2021-02-08"), as.Date("2022-04-09"), by = "1 day"),
    fill = list(n = 0))  %>%
  group_by(Country, Site) %>%
  mutate(`Cumulative enrolments` = cumsum(n)) %>%
  rename(`Calendar date` = RandDate) %>%
  ggplot(., aes(`Calendar date`, `Cumulative enrolments`)) +
  facet_wrap( ~ paste(Country, Site,  sep = ": "),
              ncol = 3, scales = "free_y") +
  geom_step() +
  geom_vline(data = md,
             aes(xintercept = as.numeric(meet_date)),
             linetype = 2) +
  scale_y_continuous(breaks = function(x) 
    unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  scale_x_date(date_breaks  = "4 months", date_labels = "%b %Y") +
  theme(strip.text = element_text(size = rel(0.7)))

Code

pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "enrolment-nepalnz-sites.pdf"), p_npnz, height = 3, width = 7)

Intervention Assignments

Code

all_data %>%
  filter_fas_itt() %>%
  dplyr::count(FAS_ITT, ACS_ITT, CAssignment) %>%
  kable() %>%
  kable_styling("striped", font_size = 14)

FAS_ITT	ACS_ITT	CAssignment	n
1	0	C0	25
1	1	C1	610
1	1	C2	613
1	1	C3	283
1	1	C4	50

Table 1: Counts of intervention assignments to anticoagulation

Intervention allocations overtime

p1 <- all_data %>%
  filter(ENR_rec == 1) %>%
  dplyr::count(Intervention = CAssignment, RandDate) %>%
  complete(Intervention,
           RandDate = seq.Date(min(RandDate, na.rm = T),
                              as.Date("2022-04-09"),
                               by = "1 day"),
           fill = list(n = 0)) %>%
  group_by(Intervention) %>%
  mutate(cn = cumsum(n)) %>%
  ungroup() %>%
  ggplot(., aes(RandDate, cn)) +
  geom_step(aes(colour = Intervention)) +
  scale_color_viridis_d(option = "D") +
  labs(x = "", y = "Cumulative allocation")
p1 / p2

Figure 4: Allocation to interventions by calendar date.

Country

Allocations to interventions by country of enrolment.

Allocations by site

all_data %>%
  filter(ENR_rec == 1) %>%
  dplyr::count(Country = PT_CountryName, Intervention = factor(CAssignment, labels = intervention_labels2()$CAssignment)) %>%
  group_by(Country) %>%
  mutate(p = n / sum(n)) %>%
  mutate(lab = sprintf("%i (%.2f)", n, p)) %>%
  select(-n, -p) %>%
  ungroup() %>%
  spread(Intervention, lab, fill = "0 (0.00)") %>%
  kable(align = "lrrrrr") %>%
  kable_styling(bootstrap_options = "striped", font_size = 12)

Country	Not randomised to anticoagulation	Low-dose	Intermediate-dose	Low-dose with aspirin	Therapeutic-dose
Australia	23 (0.15)	51 (0.34)	60 (0.39)	7 (0.05)	11 (0.07)
India	0 (0.00)	499 (0.38)	520 (0.40)	277 (0.21)	4 (0.00)
Nepal	0 (0.00)	57 (0.47)	32 (0.27)	0 (0.00)	31 (0.26)
New Zealand	2 (0.07)	12 (0.44)	8 (0.30)	1 (0.04)	4 (0.15)

Table 2: Allocation to anti-coagulation interventions by country

Allocations by country

p <- all_data %>%
  filter(ENR_rec == 1) %>%
  dplyr::count(Country = Country,
        Intervention = factor(CAssignment, labels = c("Not randomised", intervention_labels_short()$CAssignment[-1]))) %>%
  group_by(Country) %>%
  mutate(p = n / sum(n)) %>%
  ggplot(., aes(Intervention, n)) +
  facet_wrap( ~ Country, scales = "free_y", ncol = 4) +
  geom_col() +
  scale_y_continuous("Frequency",
    breaks = function(x) 
      unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25))
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "allocations-country.pdf"), p, height = 2.5, width = 7)
p

Figure 5: Allocation to anti-coagulation interventions by country

Site

Allocations to interventions by site of enrolment.

Allocations by site

p <- all_data %>%
  filter(ENR_rec == 1) %>%
  dplyr::count(Country = Country, 
        Site = Location,
        Intervention = factor(CAssignment, labels = c("Not randomised", intervention_labels_short()$CAssignment[-1]))) %>%
  group_by(Site = paste(Country, Site, sep = ": ")) %>%
  mutate(p = n / sum(n)) %>%
  ggplot(., aes(Intervention, n)) +
  facet_wrap( ~ Site, scales = "free_y") +
  geom_col() +
  scale_y_continuous("Frequency",
    breaks = function(x) 
      unique(floor(pretty(seq(0, (max(x) + 1) * 1.1))))) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25))
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "allocations-sites.pdf"), p, height = 6, width = 7)
p

Figure 6: Allocation to anti-coagulation interventions by site

Calender Time

Allocations by epoch

p <- all_data %>%
  filter(ENR_rec == 1) %>%
  dplyr::count(yr = year(RandDate), mth = month(RandDate),
        Intervention = factor(CAssignment, labels = c("Not randomised", intervention_labels_short()$CAssignment[-1]))) %>%
  group_by(yr, mth) %>%
  mutate(p = n / sum(n)) %>%
  ggplot(., aes(mth, p, fill = Intervention)) +
  facet_grid( ~ yr, drop = T, scales = "free_x", space = "free") +
  geom_col() +
  scale_y_continuous("Distribution of\ninterventions") +
  scale_fill_viridis_d(option = "B")  +
    labs(
      x = "Calendar date (month of year)") +
  scale_x_continuous(breaks = 1:12) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25))
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "allocations-calendar.pdf"), p, height = 2.5, width = 7)
p

Figure 7: Allocation to anti-coagulation interventions by country

Baseline Factors

The following provides an overview of the baseline covariates collected for individuals.

Demographics

Baseline demographics are summarised by intervention in the following table.

Demographics table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_baseline_demographics_table(sdat, format = "html")

	Anticoagulation
Variable	Low dose (n = 610)	Intermediate dose (n = 613)	Low dose with aspirin (n = 283)	Therapeutic dose (n = 50)	Overall (n = 1556)
Age (years), Median (IQR)	48 (37, 60)	48 (37, 61)	50 (38, 62)	58 (46, 69)	49 (37, 61)
Country
India, n (\%)	493 (81)	516 (84)	275 (97)	4 (8)	1288 (83)
Australia, n (\%)	49 (8)	59 (10)	7 (2)	11 (22)	126 (8)
Nepal, n (\%)	56 (9)	31 (5)	0 (0)	31 (62)	118 (8)
New Zealand, n (\%)	12 (2)	7 (1)	1 (0)	4 (8)	24 (2)
Sex
Male, n (\%)	354 (58)	387 (63)	157 (55)	25 (50)	923 (59)
Female, n (\%)	256 (42)	226 (37)	126 (45)	25 (50)	633 (41)
Weight (kg)
Median, (IQR)	68 (62, 76)	70 (62, 77)	68 (62, 76)	66 (57, 80)	69 (62, 76)
Missing, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Vaccinated¹
Yes, n (\%)	191 (31)	220 (36)	42 (15)	27 (54)	480 (31)
Missing, n (\%)	32 (5)	22 (4)	29 (10)	0 (0)	83 (5)
Ethnicity
Indian, n (\%)	494 (81)	518 (85)	275 (97)	4 (8)	1291 (83)
European, n (\%)	21 (3)	18 (3)	4 (1)	4 (8)	47 (3)
Asian, n (\%)	20 (3)	12 (2)	1 (0)	10 (20)	43 (3)
Pacific Islander, n (\%)	13 (2)	12 (2)	2 (1)	3 (6)	30 (2)
Middle Eastern, n (\%)	11 (2)	11 (2)	0 (0)	0 (0)	22 (1)
Maori, n (\%)	3 (0)	4 (1)	0 (0)	3 (6)	10 (1)
African, n (\%)	1 (0)	0 (0)	1 (0)	0 (0)	2 (0)
Aboriginal, n (\%)	0 (0)	1 (0)	0 (0)	1 (2)	2 (0)
Latin American, n (\%)	0 (0)	1 (0)	0 (0)	0 (0)	1 (0)
Other, n (\%)	45 (7)	28 (5)	0 (0)	23 (46)	96 (6)
Unknown, n (\%)	8 (1)	9 (1)	0 (0)	3 (6)	20 (1)
Smoking
Current, n (\%)	17 (3)	21 (3)	3 (1)	5 (10)	46 (3)
Former, n (\%)	74 (12)	53 (9)	15 (5)	14 (28)	156 (10)
Never, n (\%)	519 (85)	539 (88)	265 (94)	31 (62)	1354 (87)
Missing, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
¹ Site LUD did not have ethics approval for collection of vaccination status.

Table 3: Baseline demographics for participants randomised into the anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_baseline_demographics_table(sdat, format = "latex"),
  "baseline/demographics"
)

Co-morbidities

Baseline co-morbidities are summarised by anti-coagulation intervention in the following table.

Co-morbidities table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_baseline_comorbidities_table(sdat, format = "html")

	Anticoagulation
Comorbidity	Low dose (n = 610)	Intermediate dose (n = 613)	Low dose with aspirin (n = 283)	Therapeutic dose (n = 50)	Overall (n = 1556)
None, n (\%)	364 ( 60)	378 ( 62)	166 ( 59)	19 ( 38)	927 ( 60)
Hypertension, n (\%)	147 ( 24)	140 ( 23)	68 ( 24)	14 ( 28)	369 ( 24)
Diabetes, n (\%)	140 ( 23)	139 ( 23)	78 ( 28)	11 ( 22)	368 ( 24)
Obesity, n (\%)	23 ( 4)	22 ( 4)	3 ( 1)	5 ( 10)	53 ( 3)
Asthma, n (\%)	19 ( 3)	16 ( 3)	6 ( 2)	4 ( 8)	45 ( 3)
Chronic lung disease, n (\%)	16 ( 3)	13 ( 2)	1 ( 0)	7 ( 14)	37 ( 2)
Chronic cardiac disease, n (\%)	11 ( 2)	15 ( 2)	1 ( 0)	2 ( 4)	29 ( 2)
Obstructive sleep apnoea, n (\%)	3 ( 0)	2 ( 0)	2 ( 1)	0 ( 0)	7 ( 0)
Iatrogenic immunosuppression, n (\%)	1 ( 0)	6 ( 1)	0 ( 0)	0 ( 0)	7 ( 0)
Chronic kidney disease, n (\%)	0 ( 0)	5 ( 1)	1 ( 0)	0 ( 0)	6 ( 0)
Malignant neoplasm, n (\%)	1 ( 0)	2 ( 0)	0 ( 0)	1 ( 2)	4 ( 0)
Moderate or severe liver disease, n (\%)	2 ( 0)	1 ( 0)	0 ( 0)	0 ( 0)	3 ( 0)
Dialysis, n (\%)	0 ( 0)	1 ( 0)	0 ( 0)	0 ( 0)	1 ( 0)
HIV infection, n (\%)	1 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)	1 ( 0)
Dementia, n (\%)	0 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)
Missing, n (\%)	0 ( 0)	0 ( 0)	0 ( 0)	0 ( 0)	0 (0.0)

Table 4: Baseline co-morbidities for participants randomised into then anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_baseline_comorbidities_table(sdat, format = "latex"),
  "baseline/comorbidities"
)

Prognostics

Baseline prognostics are summarised by anti-coagulation intervention in the following table.

Prognostics table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_baseline_prognostics_table(sdat, format = "html")

	Anticoagulation
Variable	Low dose (n = 610)	Intermediate dose (n = 613)	Low dose with aspirin (n = 283)	Therapeutic dose (n = 50)	Overall (n = 1556)
Was the patient on room air for any of the preceding 24 hours?
Yes, n (\%)	460 (75)	460 (75)	224 (79)	39 (78)	1183 (76)
Missing, n (\%)	8 (1)	9 (1)	8 (3)	0 (0)	25 (2)
Was the patient's GCS < 15?
Yes, n (\%)	63 (10)	65 (11)	6 (2)	2 (4)	136 (9)
Missing, n (\%)	125 (20)	135 (22)	60 (21)	0 (0)	320 (21)
Peripheral oxygen saturation (SpO2) on room air (Lowest)
Median (IQR)	95 (94, 97)	96 (94, 97)	96 (94, 97)	94 (92, 96)	96 (94, 97)
Missing, n (\%)	150 (25)	153 (25)	59 (21)	11 (22)	373 (24)
Highest respiratory rate (breaths/minute)
Median (IQR)	22 (21, 25)	22 (21, 26)	22 (20, 26)	22 (20, 24)	22 (21, 26)
Missing, n (\%)	0 (0)	1 (0)	0 (0)	0 (0)	1 (0)
Highest recorded Urea in the last 24 hours (mmol/L)
Median (IQR)	4 (3, 5)	5 (4, 6)	4 (3, 6)	4 (3, 6)	4 (3, 6)
Missing, n (\%)	30 (5)	33 (5)	16 (6)	1 (2)	80 (5)
Highest recorded CRP in the last 24 hours (mg/L)
Median (IQR)	70 (37, 190)	75 (38, 220)	77 (44, 223)	68 (33, 129)	73 (39, 200)
Missing, n (\%)	74 (12)	59 (10)	18 (6)	29 (58)	180 (12)
APTT\\textsuperscript{1}
Median (IQR)	33 (29, 36)	33 (30, 36)	32 (28, 37)	33 (28, 38)	33 (29, 36)
Missing, n (\%)	430 (70)	439 (72)	195 (69)	35 (70)	1099 (71)
INR\\textsuperscript{1}
Mean (SD)	1.19 (0.39)	1.23 (0.58)	1.32 (1.34)	1.12 (0.18)	1.23 (0.72)
Missing, n (\%)	103 (17)	105 (17)	47 (17)	7 (14)	262 (17)
Fibrinogen\\textsuperscript{1} (g/L)
Mean (SD)	5.19 (2.01)	5.20 (1.60)	4.75 (1.40)	6.49 (1.52)	5.14 (1.71)
Missing, n (\%)	564 (92)	555 (91)	243 (86)	42 (84)	1404 (90)
Prothrombin time\\textsuperscript{1} (sec)
Median (IQR)	14 (13, 17)	14 (13, 17)	15 (13, 16)	13 (12, 14)	14 (13, 16)
Missing, n (\%)	193 (32)	204 (33)	116 (41)	10 (20)	523 (34)
Taking aspirin
Yes, n (\%)	20 (3)	25 (4)	2 (1)	3 (6)	50 (3)
Missing, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Time from onset of symptoms to hospitalisation
Median (IQR)	5 (3, 7)	5 (3, 6)	4 (2, 6)	4 (3, 6)	4 (3, 6)
Time from hospitalisation to randomisation
Median (IQR)	1 (0, 2)	1 (0, 2)	1 (1, 2)	1 (1, 1)	1 (0, 2)
¹ For APTT, INR, Fibrinogen, and Prothrombin only at least one required.

Table 5: Baseline prognostic variables for participants randomised into anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_baseline_prognostics_table(sdat, format = "latex"),
  "baseline/prognostics"
)

Age

Histogram of age

p_age <- all_data %>%
  filter_acs_itt() %>%
  ggplot(., aes(AgeAtEntry)) + 
  geom_histogram(
    breaks = c(18, seq(20, 100, 5)), 
    colour = "white", 
    closed="left") +
  labs(
    x = "Age at randomisation (5-year bins, 30 to 34, 35 to 39, etc.)", 
    y = "Count") +
  scale_x_continuous(breaks = seq(20, 95, 5))
pth <- file.path("outputs", "figures", "baseline")
ggsave(file.path(pth, "age-overall.pdf"), p_age, height = 2, width = 4)
p_age

Histogram of age by anti-coagulation intervention

p <- all_data %>%
  filter_acs_itt() %>%
  ggplot(., aes(AgeAtEntry)) + 
  facet_wrap( ~ CAssignment, scales = "free_y", ncol = 2) +
  geom_histogram(
    breaks = c(18, seq(20, 100, 5)), 
    colour = "white", 
    closed="left") +
  labs(
    x = "Age at randomisation (5-year bins, 30 to 34, 35 to 39, etc.)", 
    y = "Count") +
  scale_x_continuous(breaks = seq(20, 95, 5))
p

Figure 9: Distribution of age by anti-coagulation intervention

Weight

Histogram of weight

p <- all_data %>%
  filter_acs_itt() %>%
  filter(!is.na(BAS_WeightMeasurement)) %>%
  # 2 with weight reported as 0, make NA
  mutate(BAS_Weight = if_else(BAS_Weight == 0, NA_real_, BAS_Weight)) %>%
  ggplot(., aes(BAS_Weight)) +
  facet_wrap( ~ BAS_WeightMeasurement, scales = "free_y") +
  geom_histogram()
p

Date since first symptoms

Days between events for hospitalisation, randomisation, symptom onset, and first positive test.

Drugs Received During Hospital Stay

Drugs during hospital stay table

sdat <- all_data %>%
    filter_acs_itt() %>%
  mutate(CAssignment = factor(CAssignment, labels = intervention_labels()$CAssignment[-1]))
generate_discharge_drugs_table(sdat, format = "html")

	Anticoagulation
Drug received	\makecell[c]{Low dose\\(n = 610)}	\makecell[c]{Intermediate dose\\(n = 613)}	\makecell[c]{Low dose with aspirin\\(n = 283)}	\makecell[c]{Therapeutic dose\\(n = 50)}	\makecell[c]{Overall\\(n = 1556)}
Antibacterial drugs, n (\%)	449 (74)	443 (72)	231 (82)	38 (76)	1161 (75)
Antivirals
No antiviral, n (\%)	125 (20)	120 (20)	44 (16)	39 (78)	328 (21)
Camostat, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Favipiravir, n (\%)	97 (16)	100 (16)	34 (12)	0 (0)	231 (15)
Doxycycline, n (\%)	76 (12)	81 (13)	22 (8)	2 (4)	181 (12)
Ivermectin, n (\%)	201 (33)	200 (33)	91 (32)	0 (0)	492 (32)
Remdesivir, n (\%)	286 (47)	289 (47)	169 (60)	10 (20)	754 (48)
Other antiviral, n (\%)	3 (0)	1 (0)	1 (0)	0 (0)	5 (0)
Immunomodulatory
No immunomodulatory, n (\%)	60 (10)	68 (11)	38 (13)	6 (12)	172 (11)
Anakinra, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Corticosteroids, n (\%)	396 (65)	388 (63)	183 (65)	37 (74)	1004 (65)
Sarilumab, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Azithromycin, n (\%)	105 (17)	108 (18)	38 (13)	5 (10)	256 (16)
Tocilizumab, n (\%)	12 (2)	11 (2)	5 (2)	2 (4)	30 (2)
Baricitinib, n (\%)	36 (6)	50 (8)	11 (4)	6 (12)	103 (7)
Ruxolitinib, n (\%)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Tofacitinib, n (\%)	11 (2)	13 (2)	5 (2)	0 (0)	29 (2)
Zinc, n (\%)	388 (64)	394 (64)	173 (61)	13 (26)	968 (62)
Other immunomodulatory, n (\%)	20 (3)	20 (3)	11 (4)	1 (2)	52 (3)

Table 6: Drugs received during hospital stay for participants randomised into the anticoagulation domain.

Save table to outputs

save_tex_table(
  generate_discharge_drugs_table(sdat, format = "latex"),
  "baseline/drugs"
)